title: Bending the cost curve? Results from a comprehensive primary care payment pilot.
abstract: BACKGROUND
  There is much interest in understanding how using bundled primary care payments to support a patient-centered medical home (PCMH) affects total medical costs.
  
  
  METHODS
  We compare 2008-2010 claims and eligibility records on about 10,000 patients in practices transforming to a PCMH and receiving risk-adjusted base payments and bonuses, with similar data on approximately 200,000 patients of nontransformed practices remaining under fee-for-service reimbursement.
  
  
  METHODS
  We estimate the treatment effect using difference-in-differences, controlling for trend, payer type, plan type, and fixed effects. We weight to account for partial-year eligibility, use propensity weights to address differences in exogenous variables between control and treatment patients, and use the Massachusetts Health Quality Project algorithm to assign patients to practices.
  
  
  RESULTS
  Estimated treatment effects are sensitive to: control variables, propensity weighting, the algorithm used to assign patients to practices, how we address differences in health risk, and whether/how we use data from enrollees who join, leave, or change practices. Unadjusted PCMH spending reductions are 1.5% in year 1 and 1.8% in year 2. With fixed patient assignment and other adjustments, medical spending in the treatment group seems to be 5.8% (P=0.20) lower in year 1 and 8.7% (P=0.14) lower in year 2 than for propensity-weighted, continuously enrolled controls; the largest proportional 2-year reduction in spending occurs in laboratory test use (16.5%, P=0.02).
  
  
  CONCLUSIONS
  Although estimates are imprecise because of limited data and quasi-experimental design, risk-adjusted bundled payment for primary care may have dampened spending growth in 3 practices implementing a PCMH.
authors.affiliation: *Department of Economics, Boston University, Boston †Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester ‡Verisk Health Inc., Waltham, MA.
authors.forename: Sonal
authors.initials: S
authors.lastname: Vats
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.forename: Randall P
authors.initials: RP
authors.lastname: Ellis
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D019458:Insurance Coverage / Q000191:economics* / Q000706:statistics & numerical data; D007348:Insurance, Health / Q000191:economics* / Q000706:statistics & numerical data; D008297:Male; D008404:Massachusetts; D008484:Medicaid / Q000706:statistics & numerical data; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D018802:Patient-Centered Care / Q000191:economics / Q000458:organization & administration*; D011320:Primary Health Care / Q000191:economics / Q000458:organization & administration*; D057216:Propensity Score; D020379:Risk Adjustment; D014481:United States